Smart parking management plays an important role in Smart City technology. Smart parking management can reduce traffic congestion. Video sensing is an aspect of smart parking technology that enables more flexible, as well as extensible, solutions compared to in-ground metal sensors or ultrasonic sensors.
One problem with smart parking technology is that cameras often experience slow or sudden drift or shifting. Slow drift is defined as movement over the course of a day, whereas sudden drift occurs on time scales of a few minutes up to an hour. The camera drift problem significantly degrades the performance of video analytics (extraction) systems. Such drift may result from a number of factors including the camera not being correctly mounted, movement after recording starts, wind, occlusion, etc.
The region of interest (ROI) is the section of the image that captures the blockface and vehicles parked along the blockface. Successful application of computer vision vehicle detection algorithms to determine the occupancy of parked vehicles in the ROI requires that the region remains in the same location throughout the duration of the video. Drift can cause the location of the region of interest to vary from frame to frame. Thus, it is critical to correct the camera drift before further processing. Some of the hardware based solutions to this problem include the use of guide wires or other tightening means. However, these are prone to operator error, may degrade overtime, and make the workflow cumbersome.
There are several prior art solutions for video stabilization including feature point tracking, interest point tracking, and image-based registration. However, such video stabilization algorithms are often very slow and therefore not sufficient for applications that require analysis of large amounts of data quickly. In addition, such algorithms are not sufficiently robust to changes in illumination, movement, and other such factors. Accordingly, a need exists for improved methods and systems that correct camera drift.